A Mouse Model for Eosinophilic Esophagitis (EoE)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Eosinophilic esophagitis (EoE) is an emerging chronic T helper type 2 (Th2)-associated, allergic, and immune-mediated disease, characterized histologically by eosinophil-predominant mucosal inflammation and clinically by esophageal dysfunction. Over the past years, the prevalence of EoE has dramatically increased globally. Until recently, most studies of EoE focused on using human biopsies, which are also used for diagnostic purposes, or esophageal epithelial cell lines, which led to major advances in the understanding of EoE. Despite this, a robust mouse model that mimics human disease is still crucial for both understanding disease pathogenesis and as a preclinical model for testing future therapeutics. Herein, we describe a highly reproducible and robust model of EoE that can be performed using wild-type mice by ear sensitization with oxazolone (OXA) followed by intraesophageal challenges. Experimental EoE elicited by OXA mimics the main histopathological features of human EoE, including intraepithelial eosinophilia, epithelial and lamina propria thickening, basal cell hyperplasia, and fibrosis. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Induction of EoE in mice using oxazolone Support Protocol 1: Preparing the mouse esophagus for histological analysis Support Protocol 2: Assessment of epithelial and lamina propria thickness using H&E staining Support Protocol 3: Assessment of eosinophilic infiltration using anti-MBP and basal cell proliferation using anti-Ki-67 staining Support Protocol 4: Flow cytometry of mouse esophageal samples Support Protocol 5: ELISA on protein lysates of esophageal samples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it